As 360 cameras become more affordable, researchers start to explore the possibility to use 360 images for depth estimation [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. To advance the performance of 360 monocular depth estimation, Eder et al. [8]} proposed joint training of surface normal, boundary, and depth. Zeng et al. [7]} trained a network which combines 3D layout and depth. Jin et al. [10]} took advantage of the correlation between depth map and geometric structure of 360 indoor images. Cheng et al. [3]} proposed a low-cost sensing system which combines an omnidirectional camera with a calibrated projective depth camera. The 360 image and the limited FoV depth are used together as input to a CNN. Meanwhile, distortion-aware convolution filters [2]}, [13]}, [1]}, [15]} are designed to handle spherical geometric distortion.
